*/

  ANY(ZERO, ONE, SEVERAL);

  private final Set<Feature<? super Collection>> implied;
  private final @Nullable Integer numElements;

  CollectionSize(int numElements) {
    this.implied = emptySet();
    this.numElements = numElements;
  }

  CollectionSize(Feature<? super Collection>... implied) {
    // Keep the order here, so that PerCollectionSizeTestSuiteBuilder

      throws InterruptedException {
    // TODO(b/126049426): Consider using saturateToNanos(timeout) instead.
    return drain(q, buffer, numElements, timeout.toNanos(), NANOSECONDS);
  }

  /**
   * Drains the queue as {@link BlockingQueue#drainTo(Collection, int)}, but if the requested {@code

      throws InterruptedException {
    // TODO(b/126049426): Consider using saturateToNanos(timeout) instead.
    return drain(q, buffer, numElements, timeout.toNanos(), NANOSECONDS);
  }

  /**
   * Drains the queue as {@link BlockingQueue#drainTo(Collection, int)}, but if the requested {@code

 public:
  // Returns number of dimensions.
  virtual absl::Status NumDims(int* num_dims) const = 0;
  // Returns number of elements across all dimensions.
  virtual absl::Status NumElements(int64_t* num_elements) const = 0;
  // Returns size of specified dimension
  //
  // -1 indicates an unknown axis length; this is unreachable for most standard

 public:
  explicit TapeVSpace(AbstractContext* ctx) : ctx_(ctx) {}
  ~TapeVSpace() override {}

  // Returns the number of elements in the gradient tensor.
  int64_t NumElements(AbstractTensorHandle* tensor) const override;

  // Consumes references to the tensors in the gradient_tensors list and returns
  // a tensor with the result.
  AbstractTensorHandle* AggregateGradients(

    Tensor output;
    ASSERT_EQ(absl::OkStatus(), TF_TensorToTensor(dst, &output)) << line;
    ASSERT_EQ(src.NumElements(), output.NumElements()) << line;
    for (int64_t i = 0; i < src.NumElements(); ++i) {
      ASSERT_EQ(data[i], output.flat<tstring>()(i)) << line;
    }

    TF_DeleteTensor(dst);
  }
}

TEST(CAPI, TensorEncodeDecodeStrings) {
  TestEncodeDecode(__LINE__, {});

  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return -1;
  }

  int64_t num_elements = -1;
  status->status = tensorflow::unwrap(h)->NumElements(&num_elements);
  return num_elements;
}

int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index,
                            TF_Status* status) {
  if (h == nullptr) {

    status->status = result;
    return;
  }

  // Store results in c_outputs[]
  for (int i = 0; i < noutputs; ++i) {
    const Tensor& src = outputs[i];
    if (!src.IsInitialized() || src.NumElements() == 0) {
      c_outputs[i] =
          EmptyTensor(static_cast<TF_DataType>(src.dtype()), src.shape());
      continue;
    }
    c_outputs[i] = TF_TensorFromTensor(src, &status->status);